The TernausNet model was trained without pre-trained weights roughly as described previously [1]}, with modifications. Firstly, only the Pan-sharpened RGB channels were used for training, and were re-scaled to 8-bit. \(90^\circ \)  rotations, X and Y flips, imagery zooming of up to 25%, and linear brightness adjustments of up to 50% were applied randomly to training images. After augmentations, a \(512 \times 512\)  crop was randomly selected from within each \(900 \times 900\)  training chip, with one crop used per chip per training epoch. Secondly, as described in the Models section of the main text, a combination loss function was used with a weight parameter \(\alpha =0.8\) . Secondly, a variant of Adam incorporating Nesterov momentum  with default parameters was used as the optimizer. The model was trained for 25-40 epochs, and learning rate was decreased 5-fold when validation loss failed to improve for 5 epochs. Model training was halted when validation loss failed to improve for 10 epochs.
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